It is desirable to have applications running on hand-held electronic devices, such as telephones, personal digital assistants, and pad computers that can accurately recognize handwriting as these devices are becoming more popular. The users, for example, may write the characters with an electronic pen or stylus on an electronically-sensitive surface from which the handwriting is digitized and processed by the device. Alternatively, the users may write on an input surface, such as glass, with a regular pen or marker. The device then optically captures and processes the handwriting using optical character recognition methods. A common problem in handwriting recognition is the vast amount of variations in the ways individual users write characters. Several handwriting recognition technologies have been in use, but still do not provide good results, especially for cursive handwriting.
Cursive handwriting poses a significant challenge for correctly identifying a character due to variations in size, orientation and individual user idiosyncrasies in the writing. Further, users may write very differently, using angular alphabets or rectangular-edged alphabets instead of regular curves. Existing solutions typically map a sequence of writing strokes that a user makes to a previously captured image of a particular character to identify the character. This approach has a disadvantage where it can only recognize recorded images of written texts and generally not the handwriting strokes. Additionally, systems based on this technology can only handle separated alphabets rather than continuously and cursively written words and sentences. Further, these systems could only map the written characters to images of complete characters represented in a map based on feature points. The characters are then identified, for example, with Hidden Markov Modeling (HMM) methods.
Accordingly, there exists a need for an efficient method and apparatus for recognizing freestyle cursive and non-cursive handwriting without the aforementioned drawbacks.